Multi Cloud Architecture

# Multi-Cloud Architecture

Decision framework and patterns for architecting applications across AWS, Azure, and GCP.

Do not use this skill when

- The task is unrelated to multi-cloud architecture

- You need a different domain or tool outside this scope

Instructions

- Clarify goals, constraints, and required inputs.

- Apply relevant best practices and validate outcomes.

- Provide actionable steps and verification.

- If detailed examples are required, open `resources/implementation-playbook.md`.

Purpose

Design cloud-agnostic architectures and make informed decisions about service selection across cloud providers.

Use this skill when

- Design multi-cloud strategies

- Migrate between cloud providers

- Select cloud services for specific workloads

- Implement cloud-agnostic architectures

- Optimize costs across providers

Cloud Service Comparison

Compute Services

| AWS | Azure | GCP | Use Case |

|-----|-------|-----|----------|

| EC2 | Virtual Machines | Compute Engine | IaaS VMs |

| ECS | Container Instances | Cloud Run | Containers |

| EKS | AKS | GKE | Kubernetes |

| Lambda | Functions | Cloud Functions | Serverless |

| Fargate | Container Apps | Cloud Run | Managed containers |

Storage Services

| AWS | Azure | GCP | Use Case |

|-----|-------|-----|----------|

| S3 | Blob Storage | Cloud Storage | Object storage |

| EBS | Managed Disks | Persistent Disk | Block storage |

| EFS | Azure Files | Filestore | File storage |

| Glacier | Archive Storage | Archive Storage | Cold storage |

Database Services

| AWS | Azure | GCP | Use Case |

|-----|-------|-----|----------|

| RDS | SQL Database | Cloud SQL | Managed SQL |

| DynamoDB | Cosmos DB | Firestore | NoSQL |

| Aurora | PostgreSQL/MySQL | Cloud Spanner | Distributed SQL |

| ElastiCache | Cache for Redis | Memorystore | Caching |

**Reference:** See `references/service-comparison.md` for complete comparison

Multi-Cloud Patterns

Pattern 1: Single Provider with DR

- Primary workload in one cloud

- Disaster recovery in another

- Database replication across clouds

- Automated failover

Pattern 2: Best-of-Breed

- Use best service from each provider

- AI/ML on GCP

- Enterprise apps on Azure

- General compute on AWS

Pattern 3: Geographic Distribution

- Serve users from nearest cloud region

- Data sovereignty compliance

- Global load balancing

- Regional failover

Pattern 4: Cloud-Agnostic Abstraction

- Kubernetes for compute

- PostgreSQL for database

- S3-compatible storage (MinIO)

- Open source tools

Cloud-Agnostic Architecture

Use Cloud-Native Alternatives

- **Compute:** Kubernetes (EKS/AKS/GKE)

- **Database:** PostgreSQL/MySQL (RDS/SQL Database/Cloud SQL)

- **Message Queue:** Apache Kafka (MSK/Event Hubs/Confluent)

- **Cache:** Redis (ElastiCache/Azure Cache/Memorystore)

- **Object Storage:** S3-compatible API

- **Monitoring:** Prometheus/Grafana

- **Service Mesh:** Istio/Linkerd

Abstraction Layers

Application Layer
    ↓
Infrastructure Abstraction (Terraform)
    ↓
Cloud Provider APIs
    ↓
AWS / Azure / GCP

Cost Comparison

Compute Pricing Factors

- **AWS:** On-demand, Reserved, Spot, Savings Plans

- **Azure:** Pay-as-you-go, Reserved, Spot

- **GCP:** On-demand, Committed use, Preemptible

Cost Optimization Strategies

1. Use reserved/committed capacity (30-70% savings)

2. Leverage spot/preemptible instances

3. Right-size resources

4. Use serverless for variable workloads

5. Optimize data transfer costs

6. Implement lifecycle policies

7. Use cost allocation tags

8. Monitor with cloud cost tools

**Reference:** See `references/multi-cloud-patterns.md`

Migration Strategy

Phase 1: Assessment

- Inventory current infrastructure

- Identify dependencies

- Assess cloud compatibility

- Estimate costs

Phase 2: Pilot

- Select pilot workload

- Implement in target cloud

- Test thoroughly

- Document learnings

Phase 3: Migration

- Migrate workloads incrementally

- Maintain dual-r